arXiv subjects

Ganapathy Krishnamurthi

Publications and source records attributed to Ganapathy Krishnamurthi.

23 records · Page 2Linked to original sources

arXiv2018

2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation

In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We independently train liver and tumor segmentation models and cascade them for a combined segmentation of the liver and its tumor. The first stage involves segmentation of liver and the second stage uses the first stage's segmentation results for localization of liver and henceforth tumor segmentations inside liver region. The liver model was trained on the down-sampled axial slices $(256 \times 256)$, whereas for the tumor model no down-sampling of slices was done, but instead it was trained on the CT axial slices windowed at three different Hounsfield (HU) levels. On the test set our model achieved a global dice score of 0.923 and 0.625 on liver and tumor respectively. The computed tumor burden had an rmse of 0.044.

cs.CV↗

arXiv2018

Enhanced Image Classification With Data Augmentation Using Position Coordinates

In this paper we propose the use of image pixel position coordinate system to improve image classification accuracy in various applications. Specifically, we hypothesize that the use of pixel coordinates will lead to (a) Resolution invariant performance. Here, by resolution we mean the spacing between the pixels rather than the size of the image matrix. (b) Overall improvement in classification accuracy in comparison with network models trained without local pixel coordinates. This is due to position coordinates enabling the network to learn relationship between parts of objects, mimicking the human vision system. We demonstrate our hypothesis using empirical results and intuitive explanations of the feature maps learnt by deep neural networks. Specifically, our approach showed improvements in MNIST digit classification and beats state of the results on the SVHN database. We also show that the performance of our networks is unaffected despite training the same using blurred images of the MNIST database and predicting on the high resolution database.

cs.CV↗

arXiv2017

Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis

In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI). A fully automatic, voxel based classification was achieved by training a 23 layer deep FCNN on 2-D slices extracted from patient volumes. The network was trained on slices extracted from 130 patients and validated on 50 patients. For the task of survival prediction, texture and shape based features were extracted from T1 post contrast volume to train an XGBoost regressor. On BraTS 2017 validation set, the proposed scheme achieved a mean whole tumor, tumor core and active dice score of 0.83, 0.69 and 0.69 respectively and an accuracy of 52% for the overall survival prediction.

cs.CV↗

arXiv2017

Semi-supervised Learning using Denoising Autoencoders for Brain Lesion Detection and Segmentation

The work presented explores the use of denoising autoencoders (DAE) for brain lesion detection, segmentation and false positive reduction. Stacked denoising autoencoders (SDAE) were pre-trained using a large number of unlabeled patient volumes and fine tuned with patches drawn from a limited number of patients (n=20, 40, 65). The results show negligible loss in performance even when SDAE was fine tuned using 20 patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach wherein a network pre-trained with High Grade Glioma (HGG) data was fine tuned using LGG image patches. The weakly supervised SDAE (for HGG) and transfer learning based LGG network were also shown to generalize well and provide good segmentation on unseen BraTS 2013 & BraTS 2015 test data. An unique contribution includes a single layer DAE, referred to as novelty detector(ND). ND was trained to accurately reconstruct non-lesion patches using a mean squared error loss function. The reconstruction error maps of test data were used to identify regions containing lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the non-lesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.

cs.CV↗

arXiv2016

Asteroseismic determination of fundamental parameters of sun-like stars using multi-layered neural networks

The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium abundance, initial metallicity, mixing- length (assumed to be constant over time), and the age to which the star must be evolved. Some of these parameters are also very useful in characterizing the associated planets and in studying galactic archaeology. How to obtain these parameters from observations rapidly and accurately, specifically in the context of surveys of thousands of stars, is an outstanding ques- tion, one that has eluded straightforward resolution. For a given star, we typically measure the effective temperature and surface metallicity spectroscopically and low-degree oscillation frequencies through space observatories. Here we demonstrate that statistical learning, using artificial neural networks, is successful in determining the evolutionary parameters based on spectroscopic and seismic measurements. Our trained networks show robustness over a broad range of parameter space, and critically, are entirely computationally inexpensive and fully automated. We analyze the observations of a few stars using this method and the results com- pare well to inferences obtained using other techniques. This method is both computationally cheap and inferentially accurate, paving the way for analyzing the vast quantities of stellar observations from past, current, and future missions.

astro-ph.SR↗